To restore degraded images that have been deteriorated due to blur, noises, decreased resolution, or the like, an information processing device that restores images performs, for example, operations such as the following. The information processing device, first, generates a provisional restoration image (a candidate restoration image) based on a target degraded image (hereinafter, also referred to as the input image) by using initial setting parameters. Then, the information processing device generates an image which is simulated (applied) a deterioration process, such as blurring (a blurring effect), on the restoration image. Then, the information processing device corrects the restoration image such that a difference between the image generated by using simulation and the target degraded image is minimized.
However, in general, a plurality of solutions (pixel values) can be assumed for candidate solutions of a restoration image (for example, pixel values of an image) in the above processing. That is, in general, the information processing device cannot uniquely determine a solution. Thus, for example, the information processing device uses a constraint for solutions (for example, regularization) to uniquely determine a solution. In other words, the information processing device constrains solutions by using regularization, and uniquely determines a solution. The regularization used as a constraint is, for example, a constraint that suppresses variations of pixel values among adjacent pixels in a restoration image. The information processing device uniquely determines a solution as a restoration image by using the above regularization (for example, refer to PTL 1).
The technique described in PTL 1 uses a regularization strength to achieve both sharpness of a texture area and suppressing noises of a smooth area. Specifically, the technique described in PTL 1 determines a regularization strength such that a difference amount of the pixel value among adjacent pixels in a restoration image becomes large, based on the direction of the variation and the magnitude of the variation of the pixel value among the adjacent pixels for each pixel constituting an input image.
It should be noted that, in the following description, restoration of an image is also referred to as reconstruction of an image. Thus, a restoration image may also be referred to as a reconstructed image.